Deep Neural Networks for Radar Waveform Classification
This addresses the challenge of accurate radar signal classification in real-world noisy environments for applications like defense and communications.
The paper tackled the problem of classifying radar pulses from raw I/Q waveforms under noisy and unsynchronized conditions, including multiple superimposed pulses, and achieved over a 100x reduction in error-rate compared to previous state-of-the-art methods.
We consider the problem of classifying radar pulses given raw I/Q waveforms in the presence of noise and absence of synchronization. We also consider the problem of classifying multiple superimposed radar pulses. For both, we design deep neural networks (DNNs) that are robust to synchronization, pulse width, and SNR. Our designs yield more than 100x reduction in error-rate over the previous state-of-the-art.